Code Along 12 —-

STEP 2: Write ChatGPT Prompts —-

- Goal: Create ChatGPT Prompts to build a ML model

Prompt 1:

I have a dataset called attrition_raw_tbl that looks like this.

attrition_raw_tbl %>% glimpse() Rows: 1,470 Columns: 35 $ Age 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 29, 31, 34, 28, 29, 32, 22, 53, 38, 24, … $ Attrition “Yes”, “No”, “Yes”, “No”, “No”, “No”, “No”, “No”, “No”, “No”, “No”, “No”, “No”, “No”… $ BusinessTravel “Travel_Rarely”, “Travel_Frequently”, “Travel_Rarely”, “Travel_Frequently”, “Travel_… $ DailyRate 1102, 279, 1373, 1392, 591, 1005, 1324, 1358, 216, 1299, 809, 153, 670, 1346, 103, 1… $ Department ”Sales”, “Research & Development”, “Research & Development”, “Research & Development… $ DistanceFromHome 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26, 19, 24, 21, 5, 16, 2, 2, 11, 9, 7, 15, … $ Education 2, 1, 2, 4, 1, 2, 3, 1, 3, 3, 3, 2, 1, 2, 3, 4, 2, 2, 4, 3, 2, 4, 4, 2, 1, 3, 1, 4, … $ EducationField ”Life Sciences”, “Life Sciences”, “Other”, “Life Sciences”, “Medical”, “Life Science… $ EmployeeCount 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ EmployeeNumber 1, 2, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28… $ EnvironmentSatisfaction 2, 3, 4, 4, 1, 4, 3, 4, 4, 3, 1, 4, 1, 2, 3, 2, 1, 4, 1, 4, 1, 3, 1, 3, 2, 3, 2, 3, … $ Gender ”Female”, “Male”, “Male”, “Female”, “Male”, “Male”, “Female”, “Male”, “Male”, “Male”… $ HourlyRate 94, 61, 92, 56, 40, 79, 81, 67, 44, 94, 84, 49, 31, 93, 50, 51, 80, 96, 78, 45, 96, … $ JobInvolvement 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 4, 2, 3, 3, 2, 4, 4, 4, 2, 3, 4, 2, 3, 3, 3, 3, 1, 3, … $ JobLevel 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, 3, 1, 1, 4, 1, 2, 1, 3, 1, 1, 5, 1, 2, … $ JobRole “Sales Executive”, “Research Scientist”, “Laboratory Technician”, “Research Scientis… $ JobSatisfaction 4, 2, 3, 3, 2, 4, 1, 3, 3, 3, 2, 3, 3, 4, 3, 1, 2, 4, 4, 4, 3, 1, 2, 4, 1, 3, 1, 2, … $ MaritalStatus ”Single”, “Married”, “Single”, “Married”, “Married”, “Single”, “Married”, “Divorced”… $ MonthlyIncome 5993, 5130, 2090, 2909, 3468, 3068, 2670, 2693, 9526, 5237, 2426, 4193, 2911, 2661, … $ MonthlyRate 19479, 24907, 2396, 23159, 16632, 11864, 9964, 13335, 8787, 16577, 16479, 12682, 151… $ NumCompaniesWorked 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, 5, 1, 0, 1, 2, 5, 0, 7, 0, 1, 2, 4, 1, 0, … $ Over18 “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”, “Y”,… $ OverTime “Yes”, “No”, “Yes”, “Yes”, “No”, “No”, “Yes”, “No”, “No”, “No”, “No”, “Yes”, “No”, “… $ PercentSalaryHike 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13, 12, 17, 11, 14, 11, 12, 13, 16, 11, 18, … $ PerformanceRating 3, 4, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 4, 3, … $ RelationshipSatisfaction 1, 4, 2, 3, 4, 3, 1, 2, 2, 2, 3, 4, 4, 3, 2, 3, 4, 2, 3, 3, 4, 2, 3, 4, 3, 4, 2, 4, … $ StandardHours 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, … $ StockOptionLevel 0, 1, 0, 0, 1, 0, 3, 1, 0, 2, 1, 0, 1, 1, 0, 1, 2, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, … $ TotalWorkingYears 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5, 3, 6, 10, 7, 1, 31, 6, 5, 10, 13, 0, 8, … $ TrainingTimesLastYear 0, 3, 3, 3, 3, 2, 3, 2, 2, 3, 5, 3, 1, 2, 4, 1, 5, 2, 3, 3, 5, 4, 4, 6, 2, 3, 5, 2, … $ WorkLifeBalance 1, 3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, … $ YearsAtCompany 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2, 4, 10, 6, 1, 25, 3, 4, 5, 12, 0, 4, 14, 1… $ YearsInCurrentRole 4, 7, 0, 7, 2, 7, 0, 0, 7, 7, 4, 5, 2, 2, 2, 9, 2, 0, 8, 2, 2, 3, 6, 0, 2, 13, 2, 7,… $ YearsSinceLastPromotion 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, 0, 8, 0, 0, 3, 1, 1, 0, 2, 0, 1, 4, 6, 4, … $ YearsWithCurrManager 5, 7, 0, 0, 2, 6, 0, 0, 8, 7, 3, 8, 3, 2, 3, 8, 5, 0, 7, 2, 3, 3, 11, 0, 3, 8, 7, 2,…

The goal is to help predict attrition for employees.

Please write R code to create a predictive model that predicts the probability of attrition.

Load required libraries

library(dplyr) library(caret) library(glmnet)

Assuming ‘attrition_raw_tbl’ is your dataset

Data preprocessing

Remove unnecessary columns

attrition_data <- attrition_raw_tbl %>% select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours)

Convert categorical variables to factors

categorical_cols <- c(“BusinessTravel”, “Department”, “EducationField”, “Gender”, “JobRole”, “MaritalStatus”, “OverTime”) attrition_data[categorical_cols] <- lapply(attrition_data[categorical_cols], as.factor)

Split data into training and testing sets (80-20 split)

set.seed(123) train_index <- createDataPartition(attrition_data$Attrition, p = 0.8, list = FALSE) train_data <- attrition_data[train_index, ] test_data <- attrition_data[-train_index, ]

Model training

Using logistic regression with Lasso regularization (glmnet)

model <- train( Attrition ~ ., data = train_data, method = “glmnet”, trControl = trainControl(method = “cv”, number = 5), family = “binomial” )

Model evaluation

predictions <- predict(model, newdata = test_data, type = “response”) confusionMatrix(predictions, test_data$Attrition)

Prediction (optional)

Assuming ‘new_data’ is your new dataset

new_data <- new_data %>% select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours) %>% mutate_if(is.character, as.factor)

new_predictions <- predict(model, newdata = new_data, type = “response”)

Prompt 2:

Please update the code to use tidymodels instead of caret and to use the h2o model instead of glmnet.

Load required libraries

library(tidyverse) library(tidymodels) library(h2o)

Assuming ‘attrition_raw_tbl’ is your dataset

Data preprocessing

Remove unnecessary columns

attrition_data <- attrition_raw_tbl %>% select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours)

Convert categorical variables to factors

Split data into training and testing sets (80-20 split)

set.seed(123) split <- initial_split(attrition_data, prop = 0.8, strata = “Attrition”) train_data <- training(split) test_data <- testing(split)

Model training

Using h2o’s generalized linear model (GLM)

h2o.init() train_data_h2o <- as.h2o(train_data) test_data_h2o <- as.h2o(test_data)

attrition_glm <- h2o.glm( x = names(train_data_h2o)[!names(train_data_h2o) %in% “Attrition”], y = “Attrition”, training_frame = train_data_h2o, family = “binomial”, nfolds = 5 )

Model evaluation

attrition_glm_performance <- h2o.performance(attrition_glm, newdata = test_data_h2o) print(attrition_glm_performance)

Prediction (optional)

Assuming ‘new_data’ is your new dataset

new_data <- new_data %>% select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours) %>% mutate_if(is.character, as.factor)

new_data_h2o <- as.h2o(new_data)

new_predictions <- h2o.predict(attrition_glm, new_data_h2o)

Prompt 3:

Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = page, :

Load required libraries

library(tidyverse) library(tidymodels) library(h2o)

Manually set h2o cluster settings

localH2O = h2o.init(ip = “localhost”, port = 54321, nthreads = -1)

Assuming ‘attrition_raw_tbl’ is your dataset

Data preprocessing

Remove unnecessary columns

attrition_data <- attrition_raw_tbl %>% select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours)

Convert categorical variables to factors

Check if there are any missing values in Attrition column

missing_values <- sum(is.na(attrition_data$Attrition)) if (missing_values > 0) { stop(“Missing values found in the Attrition column. Please handle them before proceeding.”) }

Ensure Attrition is a binary factor

attrition_data$Attrition <- as.factor(ifelse(attrition_data$Attrition == “Yes”, 1, 0))

Split data into training and testing sets (80-20 split)

set.seed(123) split <- initial_split(attrition_data, prop = 0.8, strata = “Attrition”) train_data <- training(split) test_data <- testing(split)

Convert data to h2o frames

train_data_h2o <- as.h2o(train_data, destination_frame = “train_data_h2o”) test_data_h2o <- as.h2o(test_data, destination_frame = “test_data_h2o”)

Model training

Using h2o’s generalized linear model (GLM)

attrition_glm <- h2o.glm( x = names(train_data_h2o)[!names(train_data_h2o) %in% “Attrition”], y = “Attrition”, training_frame = train_data_h2o, family = “binomial”, nfolds = 5 )

Model evaluation

attrition_glm_performance <- h2o.performance(attrition_glm, newdata = test_data_h2o) print(attrition_glm_performance)

Prompt 4:

Please update the code to use h2o.performance in Step 5, instead of mean.

Load required libraries

library(tidyverse) library(tidymodels) library(h2o)

Manually set h2o cluster settings

localH2O = h2o.init(ip = “localhost”, port = 54321, nthreads = -1)

Assuming ‘attrition_raw_tbl’ is your dataset

Data preprocessing

Remove unnecessary columns

attrition_data <- attrition_raw_tbl %>% select(-EmployeeNumber, -EmployeeCount, -Over18, -StandardHours)

Convert categorical variables to factors

Check if there are any missing values in Attrition column

missing_values <- sum(is.na(attrition_data$Attrition)) if (missing_values > 0) { stop(“Missing values found in the Attrition column. Please handle them before proceeding.”) }

Ensure Attrition is a binary factor

attrition_data$Attrition <- as.factor(ifelse(attrition_data$Attrition == “Yes”, 1, 0))

Split data into training and testing sets (80-20 split)

set.seed(123) split <- initial_split(attrition_data, prop = 0.8, strata = “Attrition”) train_data <- training(split) test_data <- testing(split)

Convert data to h2o frames

train_data_h2o <- as.h2o(train_data, destination_frame = “train_data_h2o”) test_data_h2o <- as.h2o(test_data, destination_frame = “test_data_h2o”)

Model training

Using h2o’s generalized linear model (GLM)

attrition_glm <- h2o.glm( x = names(train_data_h2o)[!names(train_data_h2o) %in% “Attrition”], y = “Attrition”, training_frame = train_data_h2o, family = “binomial”, nfolds = 5 )

Model evaluation

attrition_glm_performance <- h2o.performance(attrition_glm, newdata = test_data_h2o) print(attrition_glm_performance)